The goal of this research is to develop quantitative image-based surrogate markers of breast cancer tumors for use in predicting response to therapy and ultimately aiding in patient management. There is a large variation in the clinical presentation of breast cancer in women, and it has been shown that in many instances, biological characteristics, i.e., features, of the primary tumor correlate with outcome. Methods to assess such biological features for the prediction of outcome, however, may be invasive, expensive or not widely available. Our hypothesis is that MRI-based features obtained through quantitative image analysis will prove useful as non-invasive biomarkers for the assessment of, and prediction of, the response of breast cancer to neoadjuvant therapy. We propose to validate such image-based biomarkers using magnetic resonance (MR) images of breast tumors from the ACRIN 6657 clinical trial, which includes pathological response data. Specifically, (1) We will investigate the relationship of breast cancer therapy outcome and MR image-based tumor characteristics (features), and changes in these features over time, using a University of Chicago database and the ACRIN 6657 I-SPY clinical trial dataset of breast cancer tumors from patients who have undergone neoadjuvant treatment, (2) We will develop and evaluate the MRI-derived `signatures' of breast cancer tumors for the prediction of, and assessment of, response to therapy using the ACRIN 6657 dataset, and (3) We will conduct preliminary, initial stratification and association of the MRI features with cancer subtype and other clinical/histopathological data from the ACRIN dataset. We will build on our 25-year history of taking innovation to the clinical setting by extending our prior development, validation, and translation of quantitative image analysis methods for computer- aided diagnosis to the post-diagnosis, predictive component in order to assess response to neoadjuvant therapy. Our research addresses the development and validation of algorithms using the existing ACRIN 6657 dataset with the goal of improving the ability to measure the response of targeted tumors to therapy quantitatively. Our proposed research is aligned with the QIN U01 PAR-11-150 goals of including robustness investigations and multi-site trial data (UChicago and ACRIN). Through this QIN grant, our participation in the QIN community will yield deliverables including an open-platform system that will provide tools for linking segmentation/feature extraction/classification, for comparing performance metrics across acquisition and/or analysis systems, and for discovery through dimension reduction techniques. Our research will yield a set of validated lesion signatures that will serve as quantitative tools for use in clinical studies/trials to predict and/or assess tumor response. Given that other studies/trials may use different treatments, we will make available to the QIN community our tools for training, testing, and presenting the the quantitative signatures so that predictive signatures for a range of treatments can be determined.